"""
v0 script written by Myriam Benisty and Stefano Facchini (Feb 2st 2022)
v1 version adapted by Stefano Facchini and Myriam Benisty (Mar 2nd 2022)
v2 version adapted to include ACA data by Jane Huang and compacted code by Gianni Cataldi (May 19th 2022)
v3 (current version) includes EB alignment by Ryan Loomis and Richard Teague (Aug 2022)


This script is written for CASA 6.2.1.7. 
It can easily be adapted to other versions of CASA, e.g. by modifying the fixvis task to phaseshift task.

The numba package is used for the alignment script, but it is not necessary (it helps with the speed-up)

The scripts are written to be compatible mpicasa.
However, after testingthe exoALMA collaboration decided not to use mpicasa in any of the calibration scripts,
since they realized that differences in the results between casa and mpicasa could arise.

Person in charge for this source:
M. Benisty

"""

import os
import numpy as np
import shutil
import matplotlib
#increase the number of figures that can be open before issuing a warning
matplotlib.rcParams.update({'figure.max_open_warning':80})

#path to your local copy of the gihub repository
#(make sure to do a 'git pull' to have the most up-to-date version)
github_path = '/users/mbenisty/calibration_scripts'
import sys
sys.path.append(github_path)
import alignment
execfile(os.path.join(github_path,'reduction_utils_exoalma.py'))

prefix = 'SY_Cha'

data_folderpath = '/lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha'
TM_names = {'LB':'TM1','SB':'TM2'}
PL_calibrated_vis = {key:os.path.join(data_folderpath,f'{TM_name}/calibrated_final.ms')
                     for key,TM_name in TM_names.items()}

for baseline_key,TM_name in TM_names.items():
    listobs(
        vis=PL_calibrated_vis[baseline_key],
        listfile=f'{prefix}_{TM_name}_calibrated_final.ms.txt',
        overwrite=True,
    )


# System properties.
incl =  51.7 #Orihara in prep.
PA = 164.7   # deg
v_sys = 4.1 # km/s from channel map of data


# Whether to run tclean in parallel or not.
use_parallel = False

fields = {'SB':'SY_Cha','LB':'SY_Cha'}

number_of_EBs = {'SB':1,'LB':4}

for baseline_key,vis in PL_calibrated_vis.items():
    split_all_obs(msfile=vis,nametemplate=f'{prefix}_{baseline_key}_EB')

#Saving observation 0 of /lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha/TM1/calibrated_final.ms to SY_Cha_LB_EB0.ms
#Saving observation 1 of /lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha/TM1/calibrated_final.ms to SY_Cha_LB_EB1.ms
#Saving observation 2 of /lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha/TM1/calibrated_final.ms to SY_Cha_LB_EB2.ms
#Saving observation 3 of /lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha/TM1/calibrated_final.ms to SY_Cha_LB_EB3.ms
#Saving observation 0 of /lustre/cv/projects/exoALMA/ALMA_PL_calibrated_data/SY_Cha/TM2/calibrated_final.ms to SY_Cha_SB_EB0.ms


for baseline_key,n_EB in number_of_EBs.items():
    for i in range(n_EB):
        vis = f'{prefix}_{baseline_key}_EB{i}.ms'
        listobs(
            vis=vis,
            listfile=f'{vis}.txt',
            overwrite=True,
        )

"""
Check that spws and field numbering is what you expect them to be now from the listobs files
In particular, make sure that there are 4 spws and that there is a single field with
field ID = 0

"""

# Line rest frequencies from splatalogue (https://splatalogue.online)
rest_freq_12CO = 345.79598990e9 #J=3-2
rest_freq_13CO = 330.58796530e9 #J=3-2 (ignoring splitting)
rest_freq_CS   = 342.88285030e9 #J=7-6

data_params_LB = {f'LB{i}': {'vis' : f'{prefix}_LB_EB{i}.ms',
                             'name' : f'LB_EB{i}',
                             'field' : fields['LB'],
                             'line_spws': np.array([0,2,3]), # list of spws containing lines
                             'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]), #frequencies (Hz) corresponding to line_spws
                             'cont_spws': None,
                             'width_array': None,
                             }
                  for i in range(number_of_EBs['LB'])}

data_params_SB = {f'SB{i}': {'vis' : f'{prefix}_SB_EB{i}.ms',
                             'name' : f'SB_EB{i}',
                             'field' : fields['SB'],
                             'line_spws': np.array([0,2,3]), # list of spws containing lines
                             'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]), #frequencies (Hz) corresponding to line_spws
                             'cont_spws': None,
                             'width_array': None,
                             }
                  for i in range(number_of_EBs['SB'])}

data_params = data_params_LB.copy()
data_params.update(data_params_SB)

figures_foldername = 'figures'
os.mkdir(figures_foldername)

def get_figures_folderpath(foldername):
    return os.path.join(figures_foldername,foldername)

def make_figures_folder(folderpath):
    if os.path.isdir(folderpath):
        print(f'going to deleted folder {folderpath} and its content')
        valid_answer = 'yes'
        answer = input(f'to confirm, type \'{valid_answer}\': ')
        if answer == valid_answer:
            shutil.rmtree(folderpath)
        else:
            print('aborting')
            return
    os.mkdir(folderpath)
    return folderpath

preselfcal_amp_figures_folder = get_figures_folderpath('1_preselfcal_amp_figures')
make_figures_folder(preselfcal_amp_figures_folder)

#adjust these plot ranges according to your data
plotranges = {'SB':[0,500,0,0.17],#xmin,xmax,ymin,ymax
              'LB':[0,2500,0,0.17]}

for params in data_params.values():
    plot_filename = prefix+'_'+params['name']+'_chan-v-amp_preselfcal.png'
    plotms(vis=params['vis'],
            xaxis='channel',
            yaxis='amplitude',
            field=params['field'],
            ydatacolumn='data',
            avgtime='1e8',
            avgscan=True,
            avgbaseline=True,
            iteraxis='spw',
            #There is a bug in plotms that plots the data wrongly if there is unequal flagging
            #between the polarisations
            #the bug occurs if we plot amp and we average over baselines
            #thus we color by polarization such that
            #we easily identify this issue
            coloraxis='corr', 
            showgui = False,
            exprange='all',
            plotfile=os.path.join(preselfcal_amp_figures_folder,plot_filename)
            )
    baseline_key,_ = params['name'].split('_')
    plotms(vis=params['vis'],
           xaxis='UVdist',
           yaxis='amplitude',
           spw='1',
           field=params['field'],
           ydatacolumn='data',
           avgtime='1e8',
           avgscan=True,
           avgchannel='3840',
           showgui = False,
           plotrange=plotranges[baseline_key],
           plotfile=os.path.join(preselfcal_amp_figures_folder,
                                 prefix+'_'+params['name']+'_uvdist-v-amp_cont_spw_preselfcal.png')
           )

for params in data_params.values():
    flagchannels_string = get_flagchannels(ms_dict=params,output_prefix=prefix,
                                           velocity_range=np.array([-15.,15.])+v_sys)
    avg_cont(ms_dict=params,output_prefix=prefix,flagchannels=flagchannels_string,
             contspws=params['cont_spws'],width_array=params['width_array'])

"""
Double-check that the channels idenfied are at the center of the spws,
due to a potential issue with the data_desc_id key in the ms table of some programs
"""
# Flagchannels input string for LB_EB0: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB1: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB2: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB3: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for SB_EB0: '0:843~3011, 2:785~3034, 3:780~3048'

#Averaged continuum dataset saved to SY_Cha_LB_EB0_initcont.ms
#Averaged continuum dataset saved to SY_Cha_LB_EB1_initcont.ms
#Averaged continuum dataset saved to SY_Cha_LB_EB2_initcont.ms
#Averaged continuum dataset saved to SY_Cha_LB_EB3_initcont.ms
#Averaged continuum dataset saved to SY_Cha_SB_EB0_initcont.ms


preselfcal_initcont_amp_folder = get_figures_folderpath('2_preselfcal_initcont_amp_figures')
make_figures_folder(preselfcal_initcont_amp_folder)

uv_ranges = {'LB':'30~60m','SB':'30~60m'} #adjust depending on amp - uv distance

for params in data_params.values():
    vis = prefix+'_'+params['name']+'_initcont.ms'
    baseline_key = params['name'].split('_')[0]
    plotms(vis=vis,
            xaxis='UVdist',
            overwrite=True,
            yaxis='amp',
            coloraxis='spw',
            avgtime='1e8',
            avgscan=True,
            showgui=False,
            plotrange=plotranges[baseline_key],
            plotfile=os.path.join(preselfcal_initcont_amp_folder,
                                  prefix+'_'+params['name']+'_uvdist-v-amp_initcont_preselfcal.png'))

    plotms(vis=vis,
            xaxis='time',
            yaxis='amp',
            avgspw=True,
            uvrange=uv_ranges[baseline_key],
            avgchannel='10000',
            avgbaseline=True,
            coloraxis='corr',
            showgui=False,
            overwrite=True,
            plotfile=os.path.join(preselfcal_initcont_amp_folder,
                                  prefix+'_'+params['name']+'_amp_v_time_initcont_preselfcal.png')
            )
#for SB, we see "waterfall features" (times where the amp suddently sharply decreases)
#we will try to fix this with self-cal


""" Define simple masks and clean scales for imaging """
mask_pa = PA #position angle of mask in degrees
mask_semimajor = 1.5 #semimajor axis of mask in arcsec
mask_semiminor = mask_semimajor*np.cos(incl/180.*np.pi) #semiminor axis of mask in arcsec
mask_ra = '10h56m30.231573s' #taken from listobs
mask_dec = '-77.11.39.34480'

mask = f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
            + f' {mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'
# Cellsize: ~beam/6-7
cellsize =  {'LB':'0.015arcsec','SB':'0.040arcsec'}
# Image size: ~primary beam 1.22*lam/A = 32'' with A=12m (19 arcsec)
imsize = {'LB':1200,'SB':400}# primary beam
scales = {'LB':[0,8,15,30,80],'SB':[0,8,15,30]}

noise_annulus = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"
thresholds = {'LB':'0.6mJy','SB':'3.0mJy'} #clean down to 6 sigma for phase cal

image_png_plot_sizes = [3,10] #sizes in arcsec of the zoomed and overview plots of the pngs

preselfcal_images_png_folder = get_figures_folderpath('3_preselfcal_images')
make_figures_folder(preselfcal_images_png_folder)

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for EB_key,p in params.items():
        imagename = prefix+'_'+p['name']+'_initcont_image'
        tclean_wrapper(
                      vis=prefix+'_'+p['name']+'_initcont.ms',
                      imagename=imagename,
                      deconvolver='multiscale',
                      scales=scales[baseline_key],
                      mask=mask,
                      threshold=thresholds[baseline_key],
                      cellsize=cellsize[baseline_key],
                      imsize=imsize[baseline_key],
                      parallel=use_parallel,
                      savemodel='modelcolumn'
                    )
        estimate_SNR(f'{imagename}.image',disk_mask=mask,noise_mask=noise_annulus)
        rms = imstat(imagename=f'{imagename}.image',region=noise_annulus)['rms'][0]
        params[EB_key]['rms'] = rms
        generate_image_png(f'{imagename}.image',plot_sizes=image_png_plot_sizes,
                            color_scale_limits=[-3*rms,10*rms],
                            save_folder=preselfcal_images_png_folder)

#LB 6x
# rms: 9.96e-02x6 = 0.597
#SB 6x
# rms: 4.96e-01x 6 = 2.976 
#SY_Cha_LB_EB0_initcont_image.image
#Beam 0.126 arcsec x 0.084 arcsec (-18.07 deg)
#Flux inside disk mask: 141.52 mJy
#Peak intensity of source: 2.27 mJy/beam
#rms: 1.06e-01 mJy/beam
#Peak SNR: 21.32
#SY_Cha_LB_EB1_initcont_image.image
#Beam 0.125 arcsec x 0.081 arcsec (12.64 deg)
#Flux inside disk mask: 135.23 mJy
#Peak intensity of source: 1.94 mJy/beam
#rms: 9.99e-02 mJy/beam
#Peak SNR: 19.45
#SY_Cha_LB_EB2_initcont_image.image
#Beam 0.125 arcsec x 0.091 arcsec (-33.36 deg)
#Flux inside disk mask: 130.31 mJy
#Peak intensity of source: 2.07 mJy/beam
#rms: 6.36e-02 mJy/beam
#Peak SNR: 32.48
#SY_Cha_LB_EB3_initcont_image.image
#Beam 0.127 arcsec x 0.093 arcsec (-9.46 deg)
#Flux inside disk mask: 137.48 mJy
#Peak intensity of source: 2.27 mJy/beam
#rms: 6.06e-02 mJy/beam
#Peak SNR: 37.39
#SY_Cha_SB_EB0_initcont_image.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 138.16 mJy
#Peak intensity of source: 24.81 mJy/beam
#rms: 4.96e-01 mJy/beam
#Peak SNR: 50.01

######
# SELF-CAL INDIVIDUAL EBs
######
""" Self-calibration parameters """
single_EB_contspws = '0~3'
single_EB_spw_mapping = [0,0,0,0]

individual_EB_selfcal_shift_folder = get_figures_folderpath('4_individual_EB_selfcal_and_shift_figures')
make_figures_folder(individual_EB_selfcal_shift_folder)

""" One round of phase-only self-cal """
for params in data_params.values():
    vis = prefix+'_'+params['name']+'_initcont.ms'
    single_EB_p1 = prefix+'_'+params['name']+'_initcont.p1'
    os.system(f'rm -rf {single_EB_p1}')
    gaincal(vis=vis,caltable=single_EB_p1,gaintype='T',spw=single_EB_contspws,
            combine='scan,spw',calmode='p',solint='inf',minsnr=4,minblperant=3)
    """ Print calibration png file """
    plotfilename = prefix+'_'+params['name']\
                        +'_initcont_gain_p1_phase_vs_time.png'
    plotms(single_EB_p1,xaxis='time',yaxis='GainPhase',overwrite=True,showgui=False,
           plotfile=os.path.join(individual_EB_selfcal_shift_folder,plotfilename))
    """ Apply the solutions """
    applycal(vis=vis,spw=single_EB_contspws,spwmap=single_EB_spw_mapping,
             gaintable=[single_EB_p1],interp='linearPD',applymode='calonly',calwt=True)
    split(vis=vis,outputvis=prefix+'_'+params['name']+'_initcont_selfcal.ms',
          datacolumn='corrected')

''' flagging:
9 of 43 solutions flagged due to SNR < 4 in spw=0 at 2021/11/25/09:23:30.7
8 of 42 solutions flagged due to SNR < 4 in spw=0 at 2021/11/25/11:03:39.6
5 of 46 solutions flagged due to SNR < 4 in spw=0 at 2021/11/28/09:24:24.5
1 of 48 solutions flagged due to SNR < 4 in spw=0 at 2021/11/29/09:24:01.6
'''

### Image self-cal'd EBs ###

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_selfcal_image'
        tclean_wrapper(vis=prefix+'_'+p['name']+'_initcont_selfcal.ms',
                        imagename=imagename,
                        deconvolver='multiscale',
                        scales=scales[baseline_key],
                        mask=mask,
                        threshold=thresholds[baseline_key],
                        cellsize=cellsize[baseline_key],
                        imsize=imsize[baseline_key],
                        parallel=use_parallel,
                        )
        output_image = f'{imagename}.image'
        estimate_SNR(output_image,disk_mask=mask,noise_mask=noise_annulus)
        rms = p['rms']
        generate_image_png(image=output_image,plot_sizes=image_png_plot_sizes,
                           color_scale_limits=[-3*rms,10*rms],
                           save_folder=individual_EB_selfcal_shift_folder)

#SY_Cha_LB_EB0_initcont_selfcal_image.image
#Beam 0.126 arcsec x 0.084 arcsec (-18.07 deg)
#Flux inside disk mask: 147.88 mJy
#Peak intensity of source: 2.31 mJy/beam
#rms: 9.63e-02 mJy/beam
#Peak SNR: 24.00
#SY_Cha_LB_EB1_initcont_selfcal_image.image
#Beam 0.125 arcsec x 0.081 arcsec (12.64 deg)
#Flux inside disk mask: 143.38 mJy
#Peak intensity of source: 1.97 mJy/beam
#rms: 9.06e-02 mJy/beam
#Peak SNR: 21.78
#SY_Cha_LB_EB2_initcont_selfcal_image.image
#Beam 0.125 arcsec x 0.091 arcsec (-33.36 deg)
#Flux inside disk mask: 135.23 mJy
#Peak intensity of source: 2.11 mJy/beam
#rms: 5.65e-02 mJy/beam
#Peak SNR: 37.33
#SY_Cha_LB_EB3_initcont_selfcal_image.image
#Beam 0.127 arcsec x 0.093 arcsec (-9.46 deg)
#Flux inside disk mask: 144.44 mJy
#Peak intensity of source: 2.32 mJy/beam
#rms: 4.84e-02 mJy/beam
#Peak SNR: 48.03
#SY_Cha_SB_EB0_initcont_selfcal_image.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 148.31 mJy
#Peak intensity of source: 26.68 mJy/beam
#rms: 1.77e-01 mJy/beam
#Peak SNR: 150.61

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_selfcal_image'
        #ratio image, to be compared to the ratio image after alignment
        ref_image = f'{prefix}_{baseline_key}_EB0_initcont_selfcal_image.image'
        ref_rms = params[f'{baseline_key}0']['rms']
        ratio_image = imagename+'.ratio'
        os.system(f'rm -rf {ratio_image}')
        immath(imagename=[ref_image,imagename+'.image'],mode='evalexpr',
               outfile=ratio_image,expr=f'iif(IM0 > 3*{ref_rms}, IM1/IM0, 0)')
        generate_image_png(ratio_image,plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                           color_scale_limits=[0.5,1.5],image_units='ratio',
                           save_folder=individual_EB_selfcal_shift_folder)


######
# ALIGN DATA (go from *initcont_selfcal.ms to *initcont_shift.ms)
######

# Select the LB EB to act as the reference (usually the best SNR one).

reference_for_LB_alignment = f'{prefix}_LB_EB3_initcont_selfcal.ms'
assert 'LB' in reference_for_LB_alignment,\
            'you need to choose an LB EB for alignment of LB'

alignment_offsets = {}

# All the other EBs will be aligned to the reference EB
# We also include the reference EB itself to make sure the coordinate changes are
# copied over.

offset_LB_EBs = ['{}_{}_initcont_selfcal.ms'.format(prefix, params['name'])
                 for params in data_params_LB.values()]

#select the continuum spw with the large bandwidth
continuum_spw_id = 1
# Find the relative offsets and update the phase centers for all offset_EBs.
npix = 1024
cell_size = 0.01
alignment.align_measurement_sets(reference_ms=reference_for_LB_alignment,
                                 align_ms=offset_LB_EBs,npix=npix,cell_size=cell_size,
                                 spwid=continuum_spw_id)


#New coordinates for SY_Cha_LB_EB0_initcont_selfcal.ms
#requires a shift of [0.019328,0.0033063]
#New coordinates for SY_Cha_LB_EB1_initcont_selfcal.ms
#requires a shift of [0.018946,-0.0031927]
#New coordinates for SY_Cha_LB_EB2_initcont_selfcal.ms
#requires a shift of [-0.0063074,0.033095]
#New coordinates for SY_Cha_LB_EB3_initcont_selfcal.ms
#no shift, reference MS.

#insert offsets from the alignment output
alignment_offsets['LB_EB0'] = [0.019328,0.0033063]
alignment_offsets['LB_EB1'] = [0.018946,-0.0031927]
alignment_offsets['LB_EB2'] = [-0.0063074,0.033095]
alignment_offsets['LB_EB3'] = [0,0]

shifted_LB_EBs = [EB.replace('.ms','_shift.ms') for EB in offset_LB_EBs]

#to check if alignment worked, calculate shift again and verify that shifts are small (i.e.
#a fraction of the cell size):

for shifted_ms in shifted_LB_EBs:
    if shifted_ms == reference_for_LB_alignment.replace('.ms','_shift.ms'):
        #for some reason the fitter fails when computing the offset of an EB to itself,
        #so we skip the ref EB
        continue
    offset = alignment.find_offset(reference_ms=reference_for_LB_alignment,
                                   offset_ms=shifted_ms,npix=npix,cell_size=cell_size,
                                   spwid=continuum_spw_id)
    print(f'#offset for {shifted_ms}: ',offset)

#offset for SY_Cha_LB_EB0_initcont_selfcal_shift.ms:  [0.00011479 0.00086661]
#offset for SY_Cha_LB_EB1_initcont_selfcal_shift.ms:  [0.00010567 0.00054159]
#offset for SY_Cha_LB_EB2_initcont_selfcal_shift.ms:  [1.16520451e-06 4.92839784e-04]

# Merge shifted LB EBs for aligning SB EBs
LB_concat_shifted = f'{prefix}_LB_concat_shifted.ms'
os.system(f'rm -rf {LB_concat_shifted}')
concat(vis=shifted_LB_EBs,concatvis=LB_concat_shifted,dirtol='0.1arcsec',
       copypointing=False)


# Align SB EBs to concat shifted LB EBs
reference_for_SB_alignment = LB_concat_shifted

offset_SB_EBs = ['{}_{}_initcont_selfcal.ms'.format(prefix, params['name'])
                 for params in data_params_SB.values()]

alignment.align_measurement_sets(reference_ms=reference_for_SB_alignment,
                                 align_ms=offset_SB_EBs,npix=npix,cell_size=cell_size,
                                 spwid=continuum_spw_id)

#New coordinates for SY_Cha_SB_EB0_initcont_selfcal.ms
#requires a shift of [0.034319,0.014483]

alignment_offsets['SB_EB0'] = [0.034319,0.014483]

shifted_SB_EBs = [EB.replace('.ms','_shift.ms') for EB in offset_SB_EBs]

#check by calculating offset again
for shifted_ms in shifted_SB_EBs:
    offset = alignment.find_offset(reference_ms=reference_for_SB_alignment,
                                   offset_ms=shifted_ms,npix=npix,cell_size=cell_size,
                                   spwid=continuum_spw_id)
    print(f'#offset for {shifted_ms}: ',offset)
#offset for SY_Cha_SB_EB0_initcont_selfcal_shift.ms:  [0.00135965 0.00321194]

# Remove the '_selfcal' part of the names to match the naming convention below.
for shifted_EB in shifted_LB_EBs+shifted_SB_EBs:
    os.system('mv {} {}'.format(shifted_EB, shifted_EB.replace('_selfcal', '')))


""" Check that the images are indeed aligned after the shift """

for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_shift_image'
        tclean_wrapper(vis=prefix+'_'+p['name']+'_initcont_shift.ms',
                        imagename=imagename,
                        deconvolver='multiscale',
                        scales=scales[baseline_key],
                        mask=mask,
                        threshold=thresholds[baseline_key],
                        cellsize=cellsize[baseline_key],
                        imsize=imsize[baseline_key],
                        parallel=use_parallel,
                      )
        estimate_SNR(f'{imagename}.image',disk_mask=mask,
                     noise_mask=noise_annulus)
        rms = p['rms']
        generate_image_png(f'{imagename}.image',plot_sizes=image_png_plot_sizes,
                           color_scale_limits=[-3*rms,10*rms],
                           save_folder=individual_EB_selfcal_shift_folder)

#SY_Cha_LB_EB0_initcont_shift_image.image
#Beam 0.126 arcsec x 0.084 arcsec (-18.07 deg)
#Flux inside disk mask: 147.71 mJy
#Peak intensity of source: 2.32 mJy/beam
#rms: 9.62e-02 mJy/beam
#Peak SNR: 24.09
#SY_Cha_LB_EB1_initcont_shift_image.image
#Beam 0.125 arcsec x 0.081 arcsec (12.64 deg)
#Flux inside disk mask: 143.42 mJy
#Peak intensity of source: 1.95 mJy/beam
#rms: 9.07e-02 mJy/beam
#Peak SNR: 21.47
#SY_Cha_LB_EB2_initcont_shift_image.image
#Beam 0.125 arcsec x 0.091 arcsec (-33.36 deg)
#Flux inside disk mask: 134.98 mJy
#Peak intensity of source: 2.12 mJy/beam
#rms: 5.60e-02 mJy/beam
#Peak SNR: 37.90
#SY_Cha_LB_EB3_initcont_shift_image.image
#Beam 0.127 arcsec x 0.093 arcsec (-9.46 deg)
#Flux inside disk mask: 144.77 mJy
#Peak intensity of source: 2.34 mJy/beam
#rms: 4.84e-02 mJy/beam
#Peak SNR: 48.42
#SY_Cha_SB_EB0_initcont_shift_image.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 148.44 mJy
#Peak intensity of source: 26.66 mJy/beam
#rms: 1.69e-01 mJy/beam
#Peak SNR: 158.08


for baseline_key,params in zip(('LB','SB'),(data_params_LB,data_params_SB)):
    for p in params.values():
        imagename = prefix+'_'+p['name']+'_initcont_shift_image'
        ref_image = f'{prefix}_{baseline_key}_EB0_initcont_shift_image.image'
        ref_rms = params[f'{baseline_key}0']['rms']
        ratio_image = imagename+'.ratio'
        os.system(f'rm -rf {ratio_image}')
        immath(imagename=[ref_image,imagename+'.image'],mode='evalexpr',
               outfile=ratio_image,expr=f'iif(IM0 > 3*{ref_rms}, IM1/IM0, 0)')
        generate_image_png(ratio_image,plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                           color_scale_limits=[0.5,1.5],image_units='ratio',
                           save_folder=individual_EB_selfcal_shift_folder)


""" Now that everything is aligned, we inspect the flux calibration. """

for params in data_params.values():
    msfile = prefix+'_'+params['name']+'_initcont_shift.ms'
    export_MS(msfile) #export MS contents into Numpy save files

""" Plot deprojected visibility profiles for all data together """
list_npz_files = []
for baseline_key,n_EB in number_of_EBs.items():
    list_npz_files += [f'{prefix}_{baseline_key}_EB{i}_initcont_shift.vis.npz'
                       for i in range(n_EB)]

deprojected_vis_profiles_folder = get_figures_folderpath('5_deprojected_vis_profiles')
make_figures_folder(deprojected_vis_profiles_folder)

plot_deprojected(filelist=list_npz_files,
                 fluxscale=[1.]*(number_of_EBs['LB']+number_of_EBs['SB']),PA=PA,
                 incl=incl,show_err=True,
                 plot_label=os.path.join(deprojected_vis_profiles_folder,
                                         f'{prefix}_flux_scale_EB_preselfcal.png'))

flux_comparison_folder = get_figures_folderpath('6_flux_comparisons')
make_figures_folder(flux_comparison_folder)

#we choose an EB to compare the flux scaling; if possible the best SB EB
flux_ref_EB = 'SB_EB0'

for params in data_params.values():
    plot_label = os.path.join(flux_comparison_folder,
                              'flux_comparison_'+params['name']+f'_to_{flux_ref_EB}.png')
    estimate_flux_scale(reference=f'{prefix}_{flux_ref_EB}_initcont_shift.vis.npz',
                        comparison=prefix+'_'+params['name']+'_initcont_shift.vis.npz',
                        incl=incl, PA=PA,plot_label=plot_label)

#The ratio of the fluxes of SY_Cha_LB_EB0_initcont_shift.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 0.95308
#The scaling factor for gencal is 0.976 for your comparison measurement
#The error on the weighted mean ratio is 1.828e-03, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of SY_Cha_LB_EB1_initcont_shift.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 0.92610
#The scaling factor for gencal is 0.962 for your comparison measurement
#The error on the weighted mean ratio is 1.885e-03, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of SY_Cha_LB_EB2_initcont_shift.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 0.86094
#The scaling factor for gencal is 0.928 for your comparison measurement
#The error on the weighted mean ratio is 1.039e-03, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of SY_Cha_LB_EB3_initcont_shift.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 0.89718
#The scaling factor for gencal is 0.947 for your comparison measurement
#The error on the weighted mean ratio is 8.498e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor

#The ratio of the fluxes of SY_Cha_SB_EB0_initcont_shift.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 1.00000
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 7.829e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor


rescale_flux(vis=prefix+'_LB_EB0_initcont_shift.ms', gencalparameter=[0.976])
rescale_flux(vis=prefix+'_LB_EB1_initcont_shift.ms', gencalparameter=[0.962])
rescale_flux(vis=prefix+'_LB_EB2_initcont_shift.ms', gencalparameter=[0.928])
rescale_flux(vis=prefix+'_LB_EB3_initcont_shift.ms', gencalparameter=[0.947])
#Splitting out rescaled values into new MS: SY_Cha_LB_EB0_initcont_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB1_initcont_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB2_initcont_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB3_initcont_shift_rescaled.ms



"""

SB has overlapping uv ranges with both ACA and LB, so
we select an SB EB as the reference for flux scaling.

In case this source does not have ACA data, simply ignore the ACA steps in the list below.
 
If all SB EBs suffer from decoherence and cannot be used, see below

We correct flux differences >4%, if phase noise looks reasonable

If you need to re-scale fluxes, use command:
rescale_flux(vis=prefix+'_LB_EB0_initcont_shift.ms', gencalparameter=[1.044])

The general procedure is as follows:
1) check flux scaling, and if flux differences are >4%, re-scale the fluxes unless
    there is clear de-coherence (e.g. seen as a systematic decrease of scaling with UV distance)
2) do not scale those EBs with phase decoherence. Proceed with self-cal following the steps below 
   to the point that those EBs are self calibrated
3) check flux scaling again
4) if no flux scaling has been applied in 1), and you now still see a flux offset, then
    apply the flux scaling determined in step 3) to the non-self caled EBs
    and repeat the self-cal

If all SB EBs suffer from phase decoherence and cannot be used as the reference
for flux scaling:
1) concat ACA data and self-cal them in phase
2) concat them to SB data and self-cal them in phase
3) check flux offsets
4) if there are flux offsets, go back to 1), but before re-starting the process apply
    the corrections to the non-self caled ACA and SB data, and re-do steps 1) - 3).
    For this you should add additional code (essentially copy/paste the code from the first
    iteration) that repeats steps 1-3, but saves all output with different filenames. Remember
    to use the right filenames when you continue the script after step 6
5) Check flux offsets of LB EBs to a correct SB EB
6) concat LB data to ACA+SB and continue with script

"""

#For SY Cha, there is no clear decoherence, so we proceed with SB self-cal after flux-scaling.
 

############ START of SB self-cal ############
#for phase cal, clean down to 6 sigma


SB_selfcal_folder = get_figures_folderpath('7_selfcal_SB_figures')
make_figures_folder(SB_selfcal_folder)

SB_cont_p0 = prefix+'_SB_contp0'
os.system('rm -rf %s.ms*' %SB_cont_p0)

concat(vis=[f'{prefix}_SB_EB{i}_initcont_shift.ms' for i in range(number_of_EBs['SB'])],
       concatvis=SB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)

listobs(vis=SB_cont_p0+'.ms',listfile=SB_cont_p0+'.ms.txt',overwrite=True)

"""Define new SB mask using new center read from listobs"""
mask_ra = '10h56m30.2385s'
mask_dec = '-77.11.39.35480'

SB_mask= f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
         +f'{mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'

noise_annulus_SB = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"

SB_tclean_wrapper_kwargs = {'mask':SB_mask,'deconvolver':'multiscale',
                            'scales':scales['SB'],'savemodel':'modelcolumn',
                            'imsize':imsize['SB'],'cellsize':cellsize['SB'],
                            'robust':0.5,'interactive':False,
                            'parallel':use_parallel,'gridder':'standard'}

# here starting again to update the threshold to 6 sigma !! 
#go down to ~6 sigma
tclean_wrapper(vis=SB_cont_p0+'.ms',imagename = 'SB_cont_p0', 
               threshold = '0.8mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p0+'.image', disk_mask = SB_mask,noise_mask = noise_annulus_SB)
#SY_Cha_SB_contp0.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 150.44 mJy
#Peak intensity of source: 26.82 mJy/beam
#rms: 1.31e-01 mJy/beam
#Peak SNR: 203.99

rms_SB = imstat(imagename = SB_cont_p0+'.image',region = noise_annulus_SB)['rms'][0]
generate_image_png(SB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)

""" Look for references antennas from weblog, and pick the first that are listed, overlapping with all EBs """
# SB EB0: DA63, DA50, DV17, DA55, DV13 
# only one EB

""" Get station numbers """
for ref_ant in ('DA63','DA50'):
    get_station_numbers(SB_cont_p0+'.ms',ref_ant)
#Observation ID 0: DA63@A035
#Observation ID 0: DA50@A040

SB_contspws = '0~3'
SB_refant   = 'DA63@A035,DA50@A040'
SB_spw_mapping = [0,0,0,0]

#First round
SB_p1 = prefix+'_SB_p1'
os.system('rm -rf '+SB_p1)
gaincal(vis=SB_cont_p0+'.ms',caltable=SB_p1,
        gaintype='G', spw=SB_contspws,refant=SB_refant, combine='scan,spw',
        calmode='p', solint='inf', minsnr=3., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')

""" Print calibration png file """
plotms(SB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,
                             f'{prefix}_SB_p1_gain_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p0+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping,gaintable=[SB_p1], interp='linearPD',
         calwt=True, applymode='calonly')

SB_cont_p1 = SB_cont_p0.replace('p0','p1')
os.system('rm -rf %s.ms*' % SB_cont_p1)
split(vis=SB_cont_p0+'.ms',outputvis=SB_cont_p1+'.ms', datacolumn='corrected')

#again threshold should be ~6sigma
tclean_wrapper(vis=SB_cont_p1+'.ms',imagename = SB_cont_p1,
               threshold = '0.8mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p1+'.image',disk_mask=SB_mask,noise_mask=noise_annulus_SB)
generate_image_png(SB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)
#SY_Cha_SB_contp1.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 150.84 mJy
#Peak intensity of source: 26.76 mJy/beam
#rms: 1.23e-01 mJy/beam
#Peak SNR: 218.01

#Second round
SB_p2 = SB_p1.replace('p1','p2')
os.system('rm -rf '+SB_p2)
gaincal(vis=SB_cont_p1+'.ms',caltable=SB_p2,
        gaintype='T',spw=SB_contspws,refant=SB_refant, combine='scan, spw',
        calmode='p', solint='360s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')
# manual flagged outliers >40

""" Print calibration png file """
plotms(SB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_p2_gain_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=SB_cont_p1+'.ms',spw=SB_contspws,
         spwmap=SB_spw_mapping, gaintable=[SB_p2], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p2 = SB_cont_p1.replace('p1','p2')
os.system('rm -rf %s.ms*' % SB_cont_p2)
split(vis=SB_cont_p1+'.ms',outputvis=SB_cont_p2+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=SB_cont_p2+'.ms',imagename = SB_cont_p2,
               threshold = '0.65mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p2+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
generate_image_png(SB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)

#SY_Cha_SB_contp2.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 151.61 mJy
#Peak intensity of source: 27.01 mJy/beam
#rms: 9.54e-02 mJy/beam
#Peak SNR: 283.02

#Third round combine spw & scan 
SB_p3 = SB_p2.replace('p2','p3')
os.system('rm -rf '+SB_p3)
gaincal(vis=SB_cont_p2+'.ms', caltable=SB_p3,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw,scan',
        calmode='p', solint='120s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
#flagged points >40                                  

""" Print calibration png file """
plotms(SB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_p3_gain_phase_vs_time.png')
       )

""" Apply the solutions """
applycal(vis=SB_cont_p2+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p3], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p3 = SB_cont_p2.replace('p2','p3')
os.system('rm -rf %s.ms*' % SB_cont_p3)
split(vis=SB_cont_p2+'.ms',outputvis=SB_cont_p3+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=SB_cont_p3+'.ms',imagename = SB_cont_p3,
               threshold = '0.6mJy', **SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p3+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
generate_image_png(SB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)
#SY_Cha_SB_contp3.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 152.07 mJy
#Peak intensity of source: 27.21 mJy/beam
#rms: 8.92e-02 mJy/beam
#Peak SNR: 304.92

#Fourth round
SB_p4 = SB_p3.replace('p3','p4')
os.system('rm -rf '+SB_p4)
gaincal(vis=SB_cont_p3+'.ms', caltable=SB_p4,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='60s', minsnr=2., minblperant=4)

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw')
#flag 3 solutions >40 

""" Print calibration png file """
plotms(SB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_p4_gain_phase_vs_time.png')
       )

applycal(vis=SB_cont_p3+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p4], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p4 = SB_cont_p3.replace('p3','p4')
os.system('rm -rf %s.ms*' % SB_cont_p4)
split(vis=SB_cont_p3+'.ms',outputvis=SB_cont_p4+'.ms',datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=SB_cont_p4+'.ms',imagename = SB_cont_p4,
               threshold = '0.53mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p4+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
generate_image_png(SB_cont_p4+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)
#SY_Cha_SB_contp4.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 152.48 mJy
#Peak intensity of source: 27.25 mJy/beam
#rms: 8.81e-02 mJy/beam
#Peak SNR: 309.26

#Fifth round
SB_p5 = SB_p4.replace('p4','p5')
os.system('rm -rf '+SB_p5)
gaincal(vis=SB_cont_p4+'.ms', caltable=SB_p5,
        gaintype='T', spw=SB_contspws,refant=SB_refant, combine='spw', calmode='p',
        solint='20s', minsnr=3., minblperant=4)


""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw')
#manually flag solutions above/below +-35 deg 

""" Print calibration png file """
plotms(SB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_SB_p5_gain_phase_vs_time.png')
       )

applycal(vis=SB_cont_p4+'.ms', spw=SB_contspws,
         spwmap = SB_spw_mapping, gaintable=[SB_p5], interp='linearPD',
         calwt=True, applymode='calonly')

""" Split off a corrected MS """
SB_cont_p5 = SB_cont_p4.replace('p4','p5')
os.system('rm -rf %s.ms*' % SB_cont_p5)
split(vis=SB_cont_p4+'.ms',outputvis=SB_cont_p5+'.ms',datacolumn='corrected')


""" Image the results; check the resulting map """
tclean_wrapper(vis=SB_cont_p5+'.ms',imagename = SB_cont_p5,
               threshold = '0.53mJy',**SB_tclean_wrapper_kwargs)
estimate_SNR(SB_cont_p5+'.image', disk_mask = SB_mask,
             noise_mask = noise_annulus_SB)
generate_image_png(SB_cont_p5+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_SB,10*rms_SB],
                   save_folder=SB_selfcal_folder)
#SY_Cha_SB_contp5.image
#Beam 0.621 arcsec x 0.437 arcsec (-0.46 deg)
#Flux inside disk mask: 152.89 mJy
#Peak intensity of source: 27.34 mJy/beam
#rms: 8.89e-02 mJy/beam
#Peak SNR: 307.68

# we tested the self cal done to 20s but the SNR doesn't improve, so we'll stop at 60s. improvement SNR from 218 to 309 from inf to 60s. 

#Check how SB selfcal improved things
#we do the check for each step of the self-cal to see how things improve at each step
non_self_caled_SB_vis = SB_cont_p0
self_caled_SB_visibilities = {'p1':SB_cont_p1,
                              'p2':SB_cont_p2,
                              'p3':SB_cont_p3,
                              'p4':SB_cont_p4}
    

SB_EBs = ('EB0')
SB_EB_spws = ('0,1,2,3')

for self_cal_step,self_caled_vis in self_caled_SB_visibilities.items():
    for EB_key,spw in zip(SB_EBs,SB_EB_spws):
        nametemplate = f'{prefix}_SB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',non_self_caled_SB_vis+'.ms']
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['SB'],output_folder=SB_selfcal_folder)

all_SB_visibilities = self_caled_SB_visibilities.copy()
all_SB_visibilities['p0'] = SB_cont_p0


for self_cal_step,vis_name in all_SB_visibilities.items():
    #split out SB EBs
    vis_ms = vis_name+'.ms'
    nametemplate = vis_ms.replace('.ms','_EB')
    split_all_obs(msfile=vis_ms,nametemplate=nametemplate)
    exported_ms = []
    for i in range(number_of_EBs['SB']):
        EB_vis = f'{nametemplate}{i}.ms'
        export_MS(EB_vis) #export MS contents into Numpy save files
        exported_ms.append(EB_vis.replace('.ms','.vis.npz'))
    for i,exp_ms in enumerate(exported_ms):
        png_filename = f'flux_comparison_SB_EB{i}_{self_cal_step}_to_{flux_ref_EB}.png'
        plot_label = os.path.join(SB_selfcal_folder,png_filename)
        estimate_flux_scale(reference=f'{prefix}_{flux_ref_EB}_initcont_shift.vis.npz',
                            comparison=exp_ms,incl=incl,PA=PA,plot_label=plot_label)
    fluxscale = [1.,]*number_of_EBs['SB']
    plot_label = os.path.join(SB_selfcal_folder,
                              f'deprojected_vis_profiles_SB_{self_cal_step}.png')
    plot_deprojected(filelist=exported_ms,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)

#Measurement set exported to SY_Cha_SB_contp1_EB0.vis.npz
#The ratio of the fluxes of SY_Cha_SB_contp1_EB0.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 1.00012
#The scaling factor for gencal is 1.000 for your comparison measurement
#The error on the weighted mean ratio is 7.830e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor
#Saving observation 0 of SY_Cha_SB_contp2.ms to SY_Cha_SB_contp2_EB0.ms

#Measurement set exported to SY_Cha_SB_contp2_EB0.vis.npz
#The ratio of the fluxes of SY_Cha_SB_contp2_EB0.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 1.00706
#The scaling factor for gencal is 1.004 for your comparison measurement
#The error on the weighted mean ratio is 7.857e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor
#Saving observation 0 of SY_Cha_SB_contp3.ms to SY_Cha_SB_contp3_EB0.ms

#Measurement set exported to SY_Cha_SB_contp3_EB0.vis.npz
#The ratio of the fluxes of SY_Cha_SB_contp3_EB0.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 1.01329
#The scaling factor for gencal is 1.007 for your comparison measurement
#The error on the weighted mean ratio is 7.882e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor
#Saving observation 0 of SY_Cha_SB_contp4.ms to SY_Cha_SB_contp4_EB0.ms

#Measurement set exported to SY_Cha_SB_contp4_EB0.vis.npz
#The ratio of the fluxes of SY_Cha_SB_contp4_EB0.vis.npz to
#SY_Cha_SB_EB0_initcont_shift.vis.npz is 1.01618
#The scaling factor for gencal is 1.008 for your comparison measurement
#The error on the weighted mean ratio is 7.893e-04, although it's likely that
#the weights in the measurement sets are too off by some constant factor
#Saving observation 0 of SY_Cha_SB_contp5.ms to SY_Cha_SB_contp5_EB0.ms

#all good

############ END of SB self-cal ############

"""SELF-CAL COMBINED DATA"""
#phase cal down to 6 sigma, amp cal down to 1 sigma

LB_selfcal_folder = get_figures_folderpath('8_selfcal_SBLB_figures')
make_figures_folder(LB_selfcal_folder)

""" Merge the SB self-cal'ed ms with LB ms"""
LB_cont_p0 = prefix+'_SBLB_contp0'
os.system('rm -rf %s.ms*' % LB_cont_p0)

#make sure you choose the right SB MS here; it depends on whether you did two
#iterations of SB self-cal to deal with decoherence
#in the case of CQ Tau, we did two iterations, so we use the "iteration2" SB_iteration2_cont_p5.ms
#in the case of SY Cha, we did only one self cal after flux scaling so we use 'iteration1' MS that is 'SB_cont_p4+'.ms'

concat(vis=[SB_cont_p4+'.ms']+[f'{prefix}_LB_EB{i}_initcont_shift_rescaled.ms' for i
                                          in range(number_of_EBs['LB'])],
            concatvis=LB_cont_p0+'.ms',dirtol='0.1arcsec',copypointing=False)


listobs(vis=LB_cont_p0+'.ms',listfile=LB_cont_p0+'.ms.txt',overwrite=True)
#25/11 - 0,1,2,3 - all LB
#25/11 - 4,5,6,7
#28/11 - 8,9,10,11
#29/11 - 12,13,14,15
#30/01 - 16,17,18,19 - SB

mask_ra = '10h56m30.2385s'
mask_dec = '-77.11.39.35480'

LB_mask = f'ellipse[[{mask_ra},{mask_dec}], [{mask_semimajor:.3f}arcsec,'\
         +f' {mask_semiminor:.3f}arcsec], {mask_pa:.1f}deg]'
noise_annulus_LB = f"annulus[[{mask_ra}, {mask_dec}],['4.arcsec', '6.arcsec']]"

LB_tclean_wrapper_kwargs = {'mask':LB_mask,'deconvolver':'multiscale',
                            'scales':scales['LB'],'savemodel':'modelcolumn',
                            'imsize':imsize['LB'],'cellsize':cellsize['LB'],
                            'robust':0.5,'interactive':False,
                            'parallel':use_parallel,'gridder':'standard'}


""" clean down to ~6 sigma"""
tclean_wrapper(vis=LB_cont_p0+'.ms', imagename = LB_cont_p0,
               threshold = '0.2mJy',**LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p0+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
rms_LB = imstat(imagename = LB_cont_p0+'.image', region = noise_annulus_LB)['rms'][0]
generate_image_png(LB_cont_p0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)

#SY_Cha_SBLB_contp0.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 159.92 mJy
#Peak intensity of source: 2.66 mJy/beam
#rms: 3.35e-02 mJy/beam
#Peak SNR: 79.30

""" Look for references antennas from weblog, and pick the first that are listed, overlapping with all EBs """
""" Since we are combined SB EBs to LB EBs, ref antennas for both SB and LB are needed """
# For LB:
# EB0: DV04, DV25, DV21, DA43, DA60
# EB1: DV04, DV25, DV21, DA43, DA57
# EB2: DA43, DA60, DA57, DV04, DV25
# EB3: DA43, DA60, DV04, DV03, DV06


#For SB we used
#Observation ID 0: DA63@A035
#Observation ID 0: DA50@A040

for ref_ant in ('DV04','DA43','DA63','DA50'):
    get_station_numbers(LB_cont_p0+'.ms',ref_ant)
#Observation ID 0: DV04@A007
#Observation ID 1: DV04@A007
#Observation ID 2: DV04@A007
#Observation ID 3: DV04@A007
#Observation ID 0: DA43@A035
#Observation ID 1: DA43@A035
#Observation ID 2: DA43@A035
#Observation ID 3: DA43@A035
#Observation ID 0: DA63@A123
#Observation ID 1: DA63@A123
#Observation ID 3: DA63@A123
#Observation ID 4: DA63@A035
#Observation ID 0: DA50@A108
#Observation ID 1: DA50@A108
#Observation ID 2: DA50@A108
#Observation ID 3: DA50@A108
#Observation ID 4: DA50@A040

""" Self-calibration parameters """
LB_contspws = '0~19'
LB_refant   = 'DV04@A007,DA43@A035,DA63@A035,DA50@A040' 

LB_spw_mapping = [0,0,0,0,4,4,4,4,8,8,8,8,12,12,12,12,16,16,16,16]

LB_p1 = prefix+'_SBLB.p1'
os.system('rm -rf '+LB_p1)
#if you get many flagged solutions, change gaintype to 'T'
gaincal(vis=LB_cont_p0+'.ms', caltable=LB_p1, gaintype='G', spw=LB_contspws,
        refant=LB_refant, combine='scan,spw', calmode='p', solint='inf',
        minsnr=3., minblperant=4)
'''
13 of 86 solutions flagged due to SNR < 3 in spw=0 at 2021/11/25/09:23:23.6
17 of 84 solutions flagged due to SNR < 3 in spw=4 at 2021/11/25/11:03:27.4
6 of 92 solutions flagged due to SNR < 3 in spw=8 at 2021/11/28/09:24:23.6
3 of 96 solutions flagged due to SNR < 3 in spw=12 at 2021/11/29/09:24:01.2
'''

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw')

""" Print calibration png file """
plotms(LB_p1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p1_gain_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p0+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p1], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p1 = prefix+'_SBLB_contp1'
os.system('rm -rf %s.ms*' % LB_cont_p1)
split(vis=LB_cont_p0+'.ms', outputvis=LB_cont_p1+'.ms', datacolumn='corrected')


""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p1+'.ms',imagename=LB_cont_p1,threshold='0.2mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p1+'.image', disk_mask=LB_mask, noise_mask=noise_annulus_LB)
generate_image_png(LB_cont_p1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)


#SY_Cha_SBLB_contp1.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 160.35 mJy
#Peak intensity of source: 2.68 mJy/beam
#rms: 3.34e-02 mJy/beam
#Peak SNR: 80.33


""" Second round of phase-only self-cal """
LB_p2 = prefix+'_SBLB.p2'
os.system('rm -rf '+LB_p2)
gaincal(vis=LB_cont_p1+'.ms', caltable=LB_p2, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw,scan', calmode='p', solint='360s',
        minsnr=2., minblperant=4) 

#quite some flagging, up to 18/46 solutions flagged

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw')

""" Print calibration png file """
plotms(LB_p2,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p2_gain_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p1+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p2], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p2 = prefix+'_SBLB_contp2'
os.system('rm -rf %s.ms*' % LB_cont_p2)
split(vis=LB_cont_p1+'.ms', outputvis=LB_cont_p2+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p2+'.ms', imagename = LB_cont_p2, threshold = '0.2mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p2+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_p2+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)

#SY_Cha_SBLB_contp2.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 160.20 mJy
#Peak intensity of source: 2.70 mJy/beam
#rms: 3.21e-02 mJy/beam
#Peak SNR: 83.77


""" Third round of phase-only self-cal """
"""
Check scan length to decide whether to combine scans. If scans are 2 min, do not combine them.
If they are shorter, combine scans here
"""
LB_p3 = prefix+'_SBLB.p3'
os.system('rm -rf '+LB_p3)
gaincal(vis=LB_cont_p2+'.ms', caltable=LB_p3, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw', calmode='p', solint='120s',
        minsnr=2., minblperant=4)
#6-15/46 solutions flagged

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p3,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p3_gain_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p2+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p3], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p3 = prefix+'_SBLB_contp3'
os.system('rm -rf %s.ms*' % LB_cont_p3)
split(vis=LB_cont_p2+'.ms', outputvis=LB_cont_p3+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = '0.19mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_p3+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)
#SY_Cha_SBLB_contp3.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 159.87 mJy
#Peak intensity of source: 2.75 mJy/beam
#rms: 3.16e-02 mJy/beam
#Peak SNR: 86.89


""" Fourth round of phase-only self-cal """ 
LB_p4 = prefix+'_SBLB.p4'
os.system('rm -rf '+LB_p4)
gaincal(vis=LB_cont_p3+'.ms', caltable=LB_p4, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw', calmode='p', solint='60s',
        minsnr=2., minblperant=4)
#10-12/46 solutions flagged

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw')
""" Print calibration png file """
plotms(LB_p4,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p4_gain_phase_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p3+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p3], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p4 = prefix+'_SBLB_contp4'
os.system('rm -rf %s.ms*' % LB_cont_p4)
split(vis=LB_cont_p3+'.ms', outputvis=LB_cont_p4+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p4+'.ms', imagename = LB_cont_p4, threshold = '0.19mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p4+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_p4+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)

#SY_Cha_SBLB_contp4.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 160.09 mJy
#Peak intensity of source: 2.70 mJy/beam
#rms: 3.24e-02 mJy/beam
#Peak SNR: 82.86
# SNR decreased! I try p5 30s. 

""" Fifth round of phase-only self-cal """
LB_p5 = prefix+'_SBLB.p5'
os.system('rm -rf '+LB_p5)
gaincal(vis=LB_cont_p4+'.ms', caltable=LB_p5, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw', calmode='p', solint='30s',
        minsnr=2., minblperant=4)
#13/46 solutions flagged


""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw')

""" Print calibration png file """
plotms(LB_p5,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p5_gain_phase_vs_time.png'))

applycal(vis=LB_cont_p4+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p5], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p5 = prefix+'_SBLB_contp5'
os.system('rm -rf %s.ms*' % LB_cont_p5)
split(vis=LB_cont_p4+'.ms', outputvis=LB_cont_p5+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p5+'.ms', imagename = LB_cont_p5, threshold = '0.19mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p5+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_p5+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)
#SY_Cha_SBLB_contp5.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 160.16 mJy
#Peak intensity of source: 2.77 mJy/beam
#rms: 3.19e-02 mJy/beam
#Peak SNR: 86.93

""" Sixth round of phase-only self-cal """
LB_p6 = prefix+'_SBLB.p6'
os.system('rm -rf '+LB_p6)
gaincal(vis=LB_cont_p5+'.ms', caltable=LB_p6, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw', calmode='p', solint='18s',
        minsnr=2., minblperant=4)
""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_p6,xaxis='time', yaxis='GainPhase',iteraxis='spw')

""" Print calibration png file """
plotms(LB_p6,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_p6_gain_phase_vs_time.png'))


applycal(vis=LB_cont_p5+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_p6], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_p6 = prefix+'_SBLB_contp6'
os.system('rm -rf %s.ms*' % LB_cont_p6)
split(vis=LB_cont_p5+'.ms', outputvis=LB_cont_p6+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_p6+'.ms', imagename = LB_cont_p6, threshold = '0.19mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p6+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_p6+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)

#SY_Cha_SBLB_contp6.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 160.06 mJy
#Peak intensity of source: 2.79 mJy/beam
#rms: 3.17e-02 mJy/beam
#Peak SNR: 88.07

# No major improvement
# we go back to SY_Cha_SBLB_cont_p3.ms 
tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = '0.19mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#SY_Cha_SBLB_contp3.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 159.87 mJy
#Peak intensity of source: 2.75 mJy/beam
#rms: 3.16e-02 mJy/beam
#Peak SNR: 86.89

""" Clean down to 1 sigma before amplitude self-cal """
tclean_wrapper(vis=LB_cont_p3+'.ms', imagename = LB_cont_p3, threshold = '0.032mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_p3+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
#SY_Cha_SBLB_contp3.image
#Beam 0.129 arcsec x 0.098 arcsec (-12.98 deg)
#Flux inside disk mask: 156.91 mJy
#Peak intensity of source: 2.80 mJy/beam
#rms: 3.12e-02 mJy/beam
#Peak SNR: 89.51

""" Amplitude self-cal"""
LB_ap0 = prefix+'_SBLB.ap0'
os.system('rm -rf '+LB_ap0)
gaincal(vis=LB_cont_p3+'.ms', caltable=LB_ap0, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw, scan', calmode='ap', solint='inf', minsnr=5.0,
        minblperant=4, solnorm=False)
'''
3 of 43 solutions flagged due to SNR < 5 in spw=0 at 2021/11/25/09:23:25.3
6 of 42 solutions flagged due to SNR < 5 in spw=4 at 2021/11/25/11:03:36.4
1 of 46 solutions flagged due to SNR < 5 in spw=8 at 2021/11/28/09:24:23.6
'''

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_ap0,xaxis='time', yaxis='GainPhase',iteraxis='spw')
plotms(LB_ap0,xaxis='time', yaxis='GainAmp',iteraxis='spw')

""" Print calibration png file """
plotms(LB_ap0,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_ap0_gain_phase_vs_time.png'))
plotms(LB_ap0,xaxis='time', yaxis='GainAmp',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_LB_ap0_gain_amp_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_p3+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_ap0], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_ap0 = prefix+'_SBLB_contap0'
os.system('rm -rf %s.ms*' % LB_cont_ap0)
split(vis=LB_cont_p3+'.ms', outputvis=LB_cont_ap0+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
#clean again down to 1 sigma
tclean_wrapper(vis=LB_cont_ap0+'.ms', imagename = LB_cont_ap0, threshold = '0.03mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_ap0+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_ap0+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)
#SY_Cha_SBLB_contap0.image
#Beam 0.130 arcsec x 0.095 arcsec (-14.94 deg)
#Flux inside disk mask: 156.28 mJy
#Peak intensity of source: 2.78 mJy/beam
#rms: 3.11e-02 mJy/beam
#Peak SNR: 89.41

""" Try ampl self-cal on scan length intervals"""
LB_ap1 = prefix+'_SBLB.ap1'
os.system('rm -rf '+LB_ap1)
gaincal(vis=LB_cont_ap0+'.ms', caltable=LB_ap1, gaintype='T', spw=LB_contspws,
        refant=LB_refant, combine='spw', calmode='ap', solint='inf', minsnr=5.0,
        minblperant=4, solnorm=False)
##about 10% flagged, but occasionally higher 
#sometimes as high as 35/42 flagged solutions, on average 11/46

""" Inspect gain tables interactively and decide whether to manually flag something"""
plotms(LB_ap1,xaxis='time', yaxis='GainPhase',iteraxis='spw')
plotms(LB_ap1,xaxis='time', yaxis='GainAmp',iteraxis='spw')

""" Print calibration png file """
plotms(LB_ap1,xaxis='time', yaxis='GainPhase',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(SB_selfcal_folder,f'{prefix}_LB_ap1_gain_phase_vs_time.png'))
plotms(LB_ap1,xaxis='time', yaxis='GainAmp',iteraxis='spw',exprange='all',
       overwrite=True,showgui=False,
       plotfile=os.path.join(LB_selfcal_folder,f'{prefix}_ap1_LB_gain_amp_vs_time.png'))

""" Apply the solutions """
applycal(vis=LB_cont_ap0+'.ms', spw=LB_contspws, spwmap = LB_spw_mapping,
         gaintable=[LB_ap1], interp='linearPD', calwt=True, applymode='calonly')

""" Split off a corrected MS """
LB_cont_ap1 = prefix+'_SBLB_contap1'
os.system('rm -rf %s.ms*' % LB_cont_ap1)
split(vis=LB_cont_ap0+'.ms', outputvis=LB_cont_ap1+'.ms', datacolumn='corrected')

""" Image the results; check the resulting map """
tclean_wrapper(vis=LB_cont_ap1+'.ms', imagename = LB_cont_ap1,threshold = '0.03mJy',
               **LB_tclean_wrapper_kwargs)
estimate_SNR(LB_cont_ap1+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_ap1+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],
                   save_folder=LB_selfcal_folder)

#SY_Cha_SBLB_contap1.image
#Beam 0.130 arcsec x 0.097 arcsec (-13.73 deg)
#Flux inside disk mask: 156.31 mJy
#Peak intensity of source: 2.78 mJy/beam
#rms: 3.09e-02 mJy/beam
#Peak SNR: 90.09

#had to flag all of EB0 and EB1, same SNR, so I skip this. I will use ap0. 

#Now check the flux scaling for LB

self_caled_LB_visibilities = {'p1':LB_cont_p1,
                              'p2':LB_cont_p2,
                              'p3':LB_cont_p3,
                              'ap0':LB_cont_ap0}

for vis in self_caled_LB_visibilities.values(): 
    listobs(vis=vis+'.ms',listfile=vis+'.ms.txt',overwrite=True)

LB_EBs = ('EB0','EB1','EB2','EB3')
LB_EB_spws = ('0,1,2,3','4,5,6,7','8,9,10,11','12,13,14,15') #fill out by referring to listobs output

for self_cal_step,self_caled_vis in self_caled_LB_visibilities.items():
    for EB_key,spw in zip(LB_EBs,LB_EB_spws):
        nametemplate = f'{prefix}_LB_{EB_key}_{self_cal_step}_compare_amp_vs_time'
        visibilities = [self_caled_vis+'.ms',LB_cont_p0+'.ms']
        plot_amp_vs_time_comparison(
                nametemplate=nametemplate,visibilities=visibilities,spw=spw,
                uvrange=uv_ranges['LB'],output_folder=LB_selfcal_folder)


#set to the EB of the combined SBLB data that corresponds to flux_ref_EB
SBLB_flux_ref_EB = 3 #this is LB EB3

total_number_of_EBs = number_of_EBs['SB'] + number_of_EBs['LB']
for self_cal_step,vis in self_caled_LB_visibilities.items():
    nametemplate = f'{prefix}_SBLB_cont{self_cal_step}_EB'
    split_all_obs(msfile=vis+'.ms',nametemplate=nametemplate)
    for i in range(total_number_of_EBs):
        export_MS(f'{nametemplate}{i}.ms')
    for i in range(total_number_of_EBs):
        reference = f'{nametemplate}{SBLB_flux_ref_EB}.vis.npz'
        output = f'flux_comparison_EB{i}_to_EB{SBLB_flux_ref_EB}'\
                       +f'_SBLB_{self_cal_step}.png'
        plot_label = os.path.join(LB_selfcal_folder,output)
        estimate_flux_scale(reference=reference,
                            comparison=f'{nametemplate}{i}.vis.npz',
                            incl=incl, PA=PA,uvbins = np.arange(40.,300.,20.),
                            plot_label=plot_label)
    filelist = [f'{nametemplate}{i}.vis.npz' for i in range(total_number_of_EBs)]
    fluxscale = [1.,]*total_number_of_EBs
    plot_label = os.path.join(LB_selfcal_folder,
                              f'deprojected_vis_profiles_SBLB_{self_cal_step}.png')
    plot_deprojected(filelist=filelist,fluxscale=fluxscale, PA=PA, incl=incl,
                     show_err=True,plot_label=plot_label)



""" Split out final continuum ms table, with a 30s timebin
"""
#stay at ap0
LB_cont_averaged = f'{prefix}_time_ave_continuum'
os.system(f'rm -rf {LB_cont_averaged}.ms*')
split(vis=LB_cont_ap0+'.ms', outputvis=LB_cont_averaged+'.ms', datacolumn='data',
      keepflags=False, timebin='30s')

"""
Now apply these solutions to the line data
"""

calibrate_linedata_folder = get_figures_folderpath('9_apply_cal_to_lines')
make_figures_folder(calibrate_linedata_folder)

""" Check that lines are not flagged in not averaged data"""
for params in data_params.values():
    plotfile = os.path.join(
                calibrate_linedata_folder,
                f'{prefix}_{params["name"]}_chan-v-amp_preselfcal_after_flagging.png')
    plotms(vis=params['vis'],
           xaxis='channel',
           yaxis='amplitude',
           field=params['field'],
           ydatacolumn='data',
           avgtime='1e8',
           avgscan=True,
           avgbaseline=True,
           coloraxis='corr',
           iteraxis='spw',
           showgui = False,
           exprange='all',
           plotfile=plotfile)


""" Apply gaintables of individual EBs"""
for params in data_params.values():
    single_EB_p1 = f'{prefix}_{params["name"]}_initcont.p1'
    vis = f'{prefix}_{params["name"]}.ms'
    applycal(vis=vis,spw='0~3',spwmap=[0,0,0,0],gaintable=[single_EB_p1],
             interp='linearPD',applymode='calonly',calwt=True)
    split(vis=vis,outputvis=vis[:-3]+'_no_ave_selfcal.ms',datacolumn='corrected')

#### SY_Cha_LB_EB0_no_ave_selfcal.ms


### ALIGN DATA ###
# we re-align the no_ave data, as we have done for the "initcont" ms tables.
# We go from *_no_ave_selfcal.ms to *_no_ave_shift.ms

reference_ms = {'LB':reference_for_LB_alignment,
                'SB':reference_for_SB_alignment}
for params in data_params.values():
    unshifted_ms = f'{prefix}_{params["name"]}_no_ave_selfcal.ms'
    array_key,_ = params['name'].split('_') #LB or SB
    offset = alignment_offsets[params['name']]
    #npix and cell_size are not needed because we do not fit any offset
    alignment.align_measurement_sets(
            reference_ms=reference_ms[array_key],align_ms=[unshifted_ms],
            align_offsets=[offset],npix=None,cell_size=None)

#applying shift [0.019328, 0.0033063] to SY_Cha_LB_EB0_no_ave_selfcal.ms
#applying shift [0.018946, -0.0031927] to SY_Cha_LB_EB1_no_ave_selfcal.ms
#applying shift [-0.0063074, 0.033095] to SY_Cha_LB_EB2_no_ave_selfcal.ms
#applying shift [0, 0] to SY_Cha_LB_EB3_no_ave_selfcal.ms
#applying shift [0.034319, 0.014483] to SY_Cha_SB_EB0_no_ave_selfcal.ms



### END OF ALIGN DATA ###

"""
re-scale the shifted *no_ave* EBs
"""
"""
If you have re-scaled fluxes, you need to re-scale the shifted *no_ave* EBs as well
"""
rescale_flux(vis=prefix+'_LB_EB0_no_ave_selfcal_shift.ms', gencalparameter=[0.976])
rescale_flux(vis=prefix+'_LB_EB1_no_ave_selfcal_shift.ms', gencalparameter=[0.962])
rescale_flux(vis=prefix+'_LB_EB2_no_ave_selfcal_shift.ms', gencalparameter=[0.928])
rescale_flux(vis=prefix+'_LB_EB3_no_ave_selfcal_shift.ms', gencalparameter=[0.947])

#Splitting out rescaled values into new MS: SY_Cha_LB_EB0_no_ave_selfcal_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB1_no_ave_selfcal_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB2_no_ave_selfcal_shift_rescaled.ms
#Splitting out rescaled values into new MS: SY_Cha_LB_EB3_no_ave_selfcal_shift_rescaled.ms


'''
for i in range(number_of_EBs['SB']):
    scaling_factor = data_params[f'SB{i}']['flux_scaling_factor']
    if scaling_factor is None:
        os.system(f'cp -rf {prefix}_SB_EB{i}_no_ave_selfcal_shift.ms {prefix}_SB_EB{i}_no_ave_selfcal_shift_rescaled.ms')
    else:
        rescale_flux(vis=f'{prefix}_SB_EB{i}_no_ave_selfcal_shift.ms',
                     gencalparameter=[scaling_factor])
'''

""" Concat not averaged SB data """
SB_combined = f'{prefix}_SB_no_ave_concat'
os.system('rm -rf %s.ms*' % SB_combined)
'''
concat(vis=[f'{prefix}_SB_EB{i}_no_ave_selfcal_shift_rescaled.ms' for i
                                in range(number_of_EBs['SB'])],
       concatvis=SB_combined+'.ms', dirtol='0.1arcsec', copypointing=False)
'''
concat(vis=[f'{prefix}_SB_EB{i}_no_ave_selfcal_shift.ms' for i
                                in range(number_of_EBs['SB'])],
       concatvis=SB_combined+'.ms', dirtol='0.1arcsec', copypointing=False)

listobs(vis=SB_combined+'.ms',listfile=SB_combined+'.ms.txt',overwrite=True)


#only one EB so only 4 spw
#I stopped at p4 for SB 
applycal(vis=SB_combined+'.ms', spw='0~3',
         gaintable=[SB_p1,SB_p2,SB_p3,SB_p4],
         spwmap = [SB_spw_mapping]*4,interp=['linearPD']*4, calwt=True,
         applymode='calonly',flagbackup=False)

SB_no_ave_selfcal = f'{prefix}_SB_no_ave_selfcal.ms'
os.system(f'rm -rf {SB_no_ave_selfcal}*')
split(vis=SB_combined+'.ms', outputvis=SB_no_ave_selfcal,datacolumn='corrected')

""" Concat not averaged LB data """

LB_combined = f'{prefix}_SBLB_no_ave_concat.ms'
os.system(f'rm -rf {LB_combined}*')

concat(vis=[SB_no_ave_selfcal]+[f'{prefix}_LB_EB{i}_no_ave_selfcal_shift_rescaled.ms' for i
                                in range(number_of_EBs['LB'])],
       concatvis=LB_combined, dirtol='0.1arcsec', copypointing=False)

listobs(vis=LB_combined,listfile=LB_combined+'.txt',overwrite=True)

applycal(vis=LB_combined, spw='0~19',
         gaintable=[LB_p1,LB_p2,LB_p3,LB_ap0],
         spwmap = [LB_spw_mapping]*4,interp=['linearPD']*4,
         calwt=True, applymode='calonly',flagbackup=False)

SBLB_no_ave_selfcal = f'{prefix}_SBLB_no_ave_selfcal_time_ave.ms'
os.system(f'rm -rf {SBLB_no_ave_selfcal}*')

""" time average of 30s, tests show there is no different with data without time average """
split(vis=LB_combined, outputvis=SBLB_no_ave_selfcal,
      datacolumn='corrected', timebin = '30s', keepflags=False)

listobs(vis=SBLB_no_ave_selfcal,listfile=SBLB_no_ave_selfcal+'.txt',overwrite=True)

# Check that solutions have been applied correctly by flagging the line data, averaging and imaging continuum
# continuum has to be the same imaged in the last step of the self-cal
complete_dataset_dict = {'vis' : SBLB_no_ave_selfcal,
                         'name' : 'SBLB_concat',
                         'field' : fields['LB'],
                         'line_spws': np.array([0,2,3,
                                                4,6,7,
                                                8,10,11,
                                                12,14,15,
                                                16,18,19]), # list of spws containing lines
                         'line_freqs': np.array([rest_freq_13CO,rest_freq_CS,rest_freq_12CO]*5), #frequencies (Hz) corresponding to line_spws
                         'cont_spws': None,
                         'width_array': None,
                         }

# Flagchannels input string for LB_EB0: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB1: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB2: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for LB_EB3: '0:842~3010, 2:786~3035, 3:779~3046'
# Flagchannels input string for SB_EB0: '0:843~3011, 2:785~3034, 3:780~3048'

#use the output of get_flagchannels at the beginning of the script to define fitspw
fitspw =  '0:842~3010, 1:0, 2:786~3035, 3:779~3046,'\
         +'4:842~3010, 5:0, 6:786~3035, 7:779~3046,'\
         +'8:842~3010, 9:0, 10:786~3035, 11:779~3046,'\
         +'12:842~3010, 13:0, 14:786~3035, 15:779~3046,'\
         +'16:843~3011, 17:0, 18:785~3034, 19:780~3048'

# avg_cont(ms_dict=complete_dataset_dict, output_prefix=prefix, flagchannels = fitspw,
#          contspws = complete_dataset_dict['cont_spws'], width_array=complete_dataset_dict['width_array'])

# image the avg then cal with same parameters as in last step of self-cal
# tclean_wrapper(vis=LB_cont_averaged+'.ms',
#                imagename = LB_cont_averaged+'_image', mask=LB_mask,
#                threshold = '0.03mJy', deconvolver='multiscale', scales=scales['LB'],
#                imsize=imsize['LB'], cellsize=cellsize['LB'],
#                robust=0.5, interactive=False, parallel=use_parallel, gridder='standard')
estimate_SNR(LB_cont_averaged+'_image'+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(LB_cont_averaged+'_image'+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],save_folder=calibrate_linedata_folder)
#SY_Cha_time_ave_continuum_image.image
#Beam 0.130 arcsec x 0.097 arcsec (-12.80 deg)
#Flux inside disk mask: 156.26 mJy
#Peak intensity of source: 2.78 mJy/beam
#rms: 3.11e-02 mJy/beam
#Peak SNR: 89.49


# image the cal then avg with same parameters as in last step of self-cal
complete_dataset_image = prefix+'_'+complete_dataset_dict['name']+'_initcont_image'
tclean_wrapper(vis=prefix+'_'+complete_dataset_dict['name']+'_initcont.ms',
               imagename = complete_dataset_image, mask=LB_mask,
               threshold = '0.03mJy', deconvolver='multiscale', scales=scales['LB'],
               imsize=imsize['LB'], cellsize=cellsize['LB'],
               robust=0.5, interactive=False, parallel=use_parallel, gridder='standard')
estimate_SNR(complete_dataset_image+'.image', disk_mask = LB_mask, noise_mask = noise_annulus_LB)
generate_image_png(complete_dataset_image+'.image',plot_sizes=image_png_plot_sizes,
                   color_scale_limits=[-3*rms_LB,10*rms_LB],save_folder=calibrate_linedata_folder)
#SY_Cha_SBLB_concat_initcont_image.image
#Beam 0.130 arcsec x 0.097 arcsec (-12.80 deg)
#Flux inside disk mask: 156.31 mJy
#Peak intensity of source: 2.78 mJy/beam
#rms: 3.11e-02 mJy/beam
#Peak SNR: 89.47

# Plot ratio of cal then avg, and avg then cal. It should be equal to ~one (only difference being the time average):
ref_image = LB_cont_averaged+'_image'+'.image'
os.system('rm -rf '+complete_dataset_image+'.ratio')
immath(imagename=[ref_image,complete_dataset_image+'.image'],mode='evalexpr',
       outfile=complete_dataset_image+'.ratio',
       expr='iif(IM0 > 3*'+str(rms_LB)+', IM1/IM0, 0)'
       )
generate_image_png(f'{complete_dataset_image}.ratio',plot_sizes=[2*mask_semimajor,2*mask_semimajor],
                   color_scale_limits=[0.5,1.5],image_units='ratio',
                   save_folder=calibrate_linedata_folder)
# Check OK, the ratio is equal to one

contsub_vis = f'{SBLB_no_ave_selfcal}.contsub'
os.system(f'rm -rf {contsub_vis}*')
uvcontsub(vis=SBLB_no_ave_selfcal, spw='0~19', fitspw=fitspw,
          excludechans=True, solint='int', fitorder=1, want_cont=False)


# Split final ms table into separate spws for 12CO, 13CO, CS and continuum

#12CO
vis_12CO = SBLB_no_ave_selfcal[:-3]+'_12CO.ms'
os.system(f'rm -rf {vis_12CO}*')
spw_12CO = '3,7,11,15,19'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_12CO,spw=spw_12CO,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_12CO}.contsub',spw=spw_12CO,
      datacolumn='data', keepflags=False)

#13CO
vis_13CO = SBLB_no_ave_selfcal[:-3]+'_13CO.ms'
os.system(f'rm -rf {vis_13CO}*')
spw_13CO = '0,4,8,12,16'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_13CO,spw=spw_13CO,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_13CO}.contsub',spw=spw_13CO,
      datacolumn='data', keepflags=False)

#CS
vis_CS = SBLB_no_ave_selfcal[:-3]+'_CS.ms'
os.system(f'rm -rf {vis_CS}*')
spw_CS = '2,6,10,14,18'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_CS,spw=spw_CS,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_CS}.contsub',spw=spw_CS,
      datacolumn='data', keepflags=False)

#continuum spw
vis_continuum = SBLB_no_ave_selfcal[:-3]+'_contspw.ms'
os.system(f'rm -rf {vis_continuum}*')
spw_continuum = '1,5,9,13,17'
split(vis=SBLB_no_ave_selfcal,outputvis=vis_continuum,spw=spw_continuum,
      datacolumn='data', keepflags=False)
split(vis=contsub_vis,outputvis=f'{vis_continuum}.contsub',spw=spw_continuum,
      datacolumn='data', keepflags=False)
